Storage Allocation Research Articles

Assessment of Flood Control Capabilities for Alternative Reservoir Storage Allocations

Multiple-purpose reservoir system operations are based on the conflicting objections of maximizing storage contents to assure high water supply reliability and maximizing empty storage space to mitigate flood risk. Reallocation of storage capacity between conservation and flood control purposes provides a strategy for optimizing limited available storage capacity in response to growing demands and changing objectives. A modeling and analysis methodology is presented in the article for assessing alternative reservoir storage allocations. Flood control capabilities are evaluated in terms of the probabilities of overtopping the storage capacities of the reservoirs in the system. Water supply capabilities are quantified in terms of reliability metrics. Flood control analysis capabilities are implemented in a modeling system originally created for detailed assessments of water supply capabilities. The methodology is applied to a system of eight multiple-purpose reservoirs in the Dallas and Fort Worth metropolitan area in the Trinity River Basin of Texas in the United States. The generalized modeling system and analysis methods are applicable to reservoir systems located anywhere including systems that may be very complex.

Storage allocation framework for designing lean buffers in forward-reserve model: a test case

PurposeThe retail revolution swing from traditional distribution to e-tailing services and unprecedented increase in internet adoption insist practitioners to diversely plan warehousing strategies. More than practically required storage space has been identified as wastes, and also it does not improve performance. An organized framework integrating storage design policies, operational performance and customer value improvement for retail-distribution management is lacking. Therefore, the purpose of this paper is to develop broad guidelines to design the “just-right” amount of forward area, i.e., “lean buffer” answering the following questions: “What should be lean buffer size? How effective the forward area is? As per demand variations, which storage waste (SKU) should be allocated with how much storage space? What is the amount of storage waste (SW)? How smooth the material flow is in between reserve-forward area?” for storage allocation in cosmetics distribution centers.Design/methodology/approachAfter forecasting static storage allocation between two planning horizons, if a particular SKU is less or non-moving, then it will cause SW, as the occupied location can be utilized by other competing SKUs, and also it impedes material flow for an instance. A dynamically efficient and self-adaptive, knapsack instance based heuristics is developed in order to make effective storage utilization.FindingsThe existing state-of-the-art under study is supported with a distribution center case, and the study investigates the need of a model adopting lean management approach in storage allocation policies along with test results in LINGO. The sensitivity analysis describes the impact of varying demand and buffer size on performance. The results are compared with uniform and exponential distributed demands, and findings reveal that the proposed heuristics improves efficiency and reduce SWs in forward-reserve area.Originality/valueThe presented model demonstrates a novel thinking of lean adoption in designing storage allocation strategy and its performance measures while reducing wastes and improving customer value. Future research issues are highlighted, which may be of great help to the researchers who would like to explore the emerging field of lean adoption for sustainable retail and distribution operations.

Multiple community energy storage planning in distribution networks using a cost-benefit analysis

This paper proposes a strategy for optimal allocation of multiple Community Energy Storage (CES) units in a distribution system with photovoltaic (PV) generation. The main contribution of this strategy is it considers all possible benefits accrued from and costs incurred by CES deployment to a utility. The benefits are gained from energy arbitrage, peaking power generation, energy loss reduction, system upgrade deferral, emission reduction and VAr support. A cost-benefit analysis is also conducted to identify the optimal Net Present Value (NPV), Discounted Payback Period (DPP) and Benefit-Cost ratio (BCR). Moreover, an optimal power factor approach is included in the analysis to dispatch CES units to improve load factors and voltage profiles. The probabilistic distribution of solar irradiance is also incorporated in the proposed strategy to consider the uncertainty of PV generation. Several sensitivity analyses are carried out to investigate the effects of PV penetration, load models and the number of CES units deployed on the profitability of CES investment. Numerical results show that the proposed strategy enables a power utility to identify the location, size and operational characteristic of CES units for maximizing the total NPV and enhancing load factors and voltage profiles.

Towards Green IoT Networking: Performance Optimization of Network Coding Based Communication and Reliable Storage

Internet of things (IoT) is expanding its outreach to almost every aspect of our daily life. By utilizing network coding in IoT, the IoT energy consumption can be reduced. Thus it is worthwhile studying and improving the applications in IoT, where network coding is incorporated. In this paper, we optimize the performance of network coding-based communication and reliable storage in two important components of IoT, including the IoT core network, where data is sensed and transmitted, and the distributed cloud storage, where the data generated by the IoT core network is stored. First, we propose an adaptive network coding scheme in the IoT core network to improve the transmission efficiency. We demonstrate the efficacy of the scheme and the performance advantage over existing schemes through simulations. Second, we introduce the optimal storage allocation problem in the network coding-based distributed cloud storage, which aims at searching for the most reliable allocation that distributes the $n$ data components into $N$ data centers, given the failure probability $p$ of each data center. Finally, we propose a polynomial-time optimal storage allocation (OSA) scheme to solve the problem. Both the theoretical analysis and the simulation results show that the storage reliability could be greatly improved by the OSA scheme.

Dynamic single node failure recovery in distributed storage systems

With the emergence of many erasure coding techniques that help provide reliability in practical distributed storage systems, we use fractional repetition coding on the given data and optimize the allocation of data blocks on system nodes in a way that minimizes the system repair cost. We selected fractional repetition coding due to its simple repair mechanism that minimizes the repair and disk access bandwidths together with the property of un-coded repair process. To minimize the system repair cost, we formulate our problem using incidence matrices and solve it heuristically using genetic algorithms for all possible cases of single node failures. We then address three practical extensions that respectively account for newly arriving blocks, newly arriving nodes and variable priority files. A re-optimization mechanism for the storage allocation matrix is proposed for the first two extensions that can be easily implemented in real time without the need to redistribute original on-node blocks. The third extension is addressed by implementing variable fractional repetition codes which is shown to achieve significant cost reduction. The contributions of the paper are four fold: (i) generating an optimized block distribution scheme among the nodes of a given data center for fixed and variable size blocks; (ii) optimization of storage allocation under dynamic environments with data block arrivals; (iii) optimization of storage allocation with newly added storage nodes; and (iv) generating an effective block distribution scheme among the nodes by accounting for varying priorities among data blocks. We present a wide range of results for the various proposed algorithms and considered scenarios to quantify the achievable performance gains.

Data mining-based algorithm for storage location assignment in a randomised warehouse

Data mining has long been applied in information extraction for a wide range of applications such as customer relationship management in marketing. In the retailing industry, this technique is used to extract the consumers buying behaviour when customers frequently purchase similar products together; in warehousing, it is also beneficial to store these correlated products nearby so as to reduce the order picking operating time and cost. In this paper, we present a data mining-based algorithm for storage location assignment of piece picking items in a randomised picker-to-parts warehouse by extracting and analysing the association relationships between different products in customer orders. The algorithm aims at minimising the total travel distances for both put-away and order picking operations. Extensive computational experiments based on synthetic data that simulates the operations of a computer and networking products spare parts warehouse in Hong Kong have been conducted to test the effectiveness and applicability of the proposed algorithm. Results show that our proposed algorithm is more efficient than the closest open location and purely dedicated storage allocation systems in minimising the total travel distances. The proposed storage allocation algorithm is further evaluated with experiments simulating larger scale warehouse operations. Similar results on the performance comparison among the three storage approaches are observed. It supports the proposed storage allocation algorithm and is applicable to improve the warehousing operation efficiency if items have strong association among each other.

Storage Allocation in Automated Container Terminals: the Upper Level

Abstract Nowadays automation is a trend of container terminals all over the world. Although not applied in current automated container terminals, storage allocation is indispensable in conventional container terminals, and promising for automated container terminals in future. This paper seeks into the storage allocation problem in automated container terminals and proposed a two level structure for the problem. A mixed integer programming model is built for the upper level, and a modified Particle Swarm Optimization (PSO) algorithm is applied to solve the model. The applicable conditions of the model is investigated by numerical experiments, so as the performance of the algorithm in different problem scales. It is left to future research the lower level of the problem and the potential benefit of storage allocation to automated container terminals.

Optimal Storage Allocation for Wireless Cloud Caching Systems With a Limited Sum Storage Capacity

In wireless cloud storage systems, the recovery failure probability depends on not only wireless channel conditions but also storage size of each distributed storage node. For an efficient utilization of limited storage capacity and the performance characterization of allocation strategies, we asymptotically analyze the recovery failure probability of a wireless cloud storage system with a sum storage capacity constraint for both high SNR regime and low SNR regime. Then, we find the optimal storage allocation strategy across distributed storage nodes in terms of the asymptotic recovery failure probability. Our analysis reveals that the maximal symmetric allocation is optimal for high SNR regime and the minimal allocation (with $\lfloor T\rfloor$ complete storage nodes and an incomplete storage node) is optimal for low SNR regime, where $T$ is the sum storage capacity. Based on the numerical investigation, we also show that in intermediate SNR regime, a balance allocation between the minimal allocation and the maximal symmetric allocation would not be required if we select one between them according to SNR.

A Fast-Multipole Unified Technique For The Analysis Of Potential Problems With The Boundary Element Methods

The proposed developments are based on a consistent implementation of the conventional, collocation boundary element method (BEM). A scheme is used to expand a generic (not problem-dependent) fundamental solution about hierarchical levels of source and field poles, which is particularly advantageous to make the technique seamlessly applicable to 2D and 3D problems of elasticity or potential, in terms of different types of curved elements for generally complicated geometry and topology. The proposed compact algorithm is more straightforward to lay out and seems to be more efficient than the ones available in the technical literature – particularly because the outermost loop refers to field nodes and geometry, in what may be called a reverse implementation. Some numerical results are shown for the conventional BEM, with validation and assessment for a few simple, but very large-scale, 2D potential problems with complicated geometry and topology for constant, linear and quadratic elements. Since iterative solvers are not required in this first step of numerical simulations, an isolated assessment of accuracy, computational effort and storage allocation of the proposed fast multipole technique becomes possible.

Content-aware replica placement strategy for IPTV services over peer-service area architecture

Due to the enormous improvement in networking and multimedia, IPTV has become recently a popular means to distribute high quality TV services over IP networks. Accordingly, Telecommunication companies started the competition to provide IPTV services to increase their customer base and profit. The key concern of service providers in this hectic competition is to provide high quality service with lower cost. However, the contents' popularity and the users' preferences are fluctuated rapidly, which leads to resources waste and load imbalance. Thus, the contents' status should be considered during the content replication to save resources and reduce service cost. To the best of our knowledge, there is no work investigate the impact of contents' status on building Replica Placement Strategy. Therefore, this paper studies the impact of contents' status on replica placement strategy over the peer-service area architecture. Two optimization models are proposed Cost Effective Model (CE), which replicates the contents partially without considering contents' status and Cost Effective with Load Balance model (CELB), which considers the contents' status. Both models have been solved using Hybrid Genetic Algorithm. The experimental results show that CELB model outperforms the other models in terms of Storage Saving Ratio (SSR), load distribution, and allocation cost.

Spatial distribution of phlorotannins and its relationship with photosynthetic UV tolerance and allocation of storage carbohydrates in blades of the kelp Lessonia spicata

The action of an intercalary meristem in the fronds determines the growth patterns of the sub-Antarctic kelp Lessonia spicata, which result in gradients in physiological activity and tissue composition. In this study, the allocation of phenolic compounds (phlorotannins) was examined in relation with the longitudinal patterns of photosynthesis (as 14C fixation and chlorophyll fluorescence) and storage of carbohydrates (mannitol and laminaran) in the blades. Moreover, exposures to UV radiation were carried out in order to test the photoprotective role of phlorotannins along different blade regions. The content of soluble phlorotannins was higher in the basal regions than in the middle and apical parts, which was correlated with the longitudinal allocation of mannitol and light-independent 14C fixation, two indicators of growth activity. In contrast, photosynthetic 14C fixation and allocation of laminaran were inversely related to the levels of phlorotannins along the blade. The photosynthetic characteristics measured using Imaging-PAM fluorescence did not show clear intra-blade patterns. UV exposures resulted in a decline in chlorophyll fluorescence (F v/F m) after 24 and 48 h (10 and 20 % respectively); however, no differential effects in different regions of the fronds were observed. The content of soluble phlorotannins in response to UV radiation was mostly determined by the time of exposure and less by the blade region: After a 6-h exposure phlorotannins decreased around 40 %, while after 48 h UV radiation stimulated the synthesis of phlorotannins by 22–99 %. Overall, after a 12-h UV exposure an increasing tendency of phlorotannins allocated in the basal regions of the blade was demonstrated. This study reinforces the idea that phlorotannins are important not only as secondary metabolites, but also their allocation in the thallus of kelps is integrated within the morpho-functional characteristics related to photosynthesis and biomass formation.

Power factor‐based scheduling of distributed battery energy storage units optimally allocated in bulk power systems for mitigating marginal losses

This study addresses the problem of multi-objective optimal allocation and management of multiple battery energy storage (BES) units. The multi-objective genetic algorithm is first used to find the so-called Pareto front while minimisation of power losses and the total installed capacity of the BES units are simultaneous objective functions. A number of solutions are chosen and developed over one year to find the best schedule for BES utilisation taking into account the power factor (PF) of the charge and discharge modes. Results of studies on the IEEE reliability test system 1996 confirm the existence of an optimal solution for loss reduction. Optimal tuning of the charge and discharge PFs has also proven effective for marginal loss reduction and saving energy every day of the year. Finally, it is shown that the power loss would decrease, even during charge hours, if PFs of BESs are optimally tuned.

Reservoir Operating Rule Optimization for California’s Sacramento Valley

doi: http://dx.doi.org/10.15447/sfews.2016v14iss1art6 Reservoir operating rules for water resource systems are typically developed by combining intuition, professional discussion, and simulation modeling. This paper describes a joint optimization–simulation approach to develop preliminary economically-based operating rules for major reservoirs in California’s Sacramento Valley, based on optimized results from CALVIN, a hydro-economic optimization model. We infer strategic operating rules from the optimization model results, including storage allocation rules to balance storage among multiple reservoirs, and reservoir release rules to determine monthly release for individual reservoirs. Results show the potential utility of considering previous year type on water availability and various system and sub-system storage conditions, in addition to normal consideration of local reservoir storage, season, and current inflows. We create a simple simulation to further refine and test the derived operating rules. Optimization model results show particular insights for balancing the allocation of water storage among Shasta, Trinity, and Oroville reservoirs over drawdown and refill seasons, as well as some insights for release rules at major reservoirs in the Sacramento Valley. We also discuss the applicability and limitations of developing reservoir operation rules from optimization model results.

A Constant Factor Approximation Algorithm for the Storage Allocation Problem

We study the storage allocation problem (SAP) which is a variant of the unsplittable flow problem on paths (UFPP). A SAP instance consists of a path $$P = (V,E)$$P=(V,E) and a set J of tasks. Each edge $$e \in E$$e?E has a capacity $$c_e$$ce and each task $$j \in J$$j?J is associated with a path $$I_j$$Ij in P, a demand $$d_j$$dj and a weight $$w_j$$wj. The goal is to find a maximum weight subset $$S \subseteq J$$S⊆J of tasks and a height function $$h:S \rightarrow \mathbb {R}^+$$h:S?R+ such that (i) $$h(j)+d_j \le c_e$$h(j)+dj≤ce, for every $$e \in I_j$$e?Ij; and (ii) if $$j,i \in S$$j,i?S such that $$I_j \cap I_i \ne \emptyset $$Ij?Ii?? and $$h(j) \ge h(i)$$h(j)?h(i), then $$h(j) \ge h(i) + d_i$$h(j)?h(i)+di. SAP can be seen as a rectangle packing problem in which rectangles can be moved vertically, but not horizontally. We present a polynomial time $$(9+\varepsilon )$$(9+?)-approximation algorithm for SAP. Our algorithm is based on a variation of the framework for approximating UFPP by Bonsma et al. [FOCS 2011] and on a $$(4+\varepsilon )$$(4+?)-approximation algorithm for $$\delta $$?-small SAP instances (in which $$d_j \le \delta \cdot c_e$$dj≤?·ce, for every $$e \in I_j$$e?Ij, for a sufficiently small constant $$\delta >0$$?>0). In our algorithm for $$\delta $$?-small instances, tasks are packed carefully in strips in a UFPP manner, and then a $$(1+\varepsilon )$$(1+?) factor is incurred by a reduction from SAP to UFPP in strips. The strips are stacked to form a SAP solution. Finally, we provide a $$(10+\varepsilon )$$(10+?)-approximation algorithm for SAP on ring networks.

On a storage allocation model with finite capacity

We consider a storage allocation model with a finite number of storage spaces. There are m primary spaces that are ranked {1,2,. . .,m} and R secondary spaces ranked {m + 1, m + 2,. . .,m + R}. Items arrive according to a Poisson process, occupy a space for a random exponentially distributed time, and an arriving item takes the lowest ranked available space. Letting N1 and N2 denote the numbers of occupied primary and secondary spaces, we study the joint distribution Prob[N1 = k, N2 = r] in the steady state. The joint process (N1, N2) behaves as a random walk in a lattice rectangle. We shall obtain explicit expressions for the distribution of (N1, N2), as well as the marginal distribution of N2. We also give some numerical studies to illustrate the qualitative behaviors of the distribution(s). The main contribution is to study the effects of a finite secondary capacity R, whereas previous studies had R = ∞.

Optimal Coding and Allocation for Perfect Secrecy in Multiple Clouds

For a user to store data in the cloud, using services provided by multiple cloud storage providers (CSPs) is a promising approach to increase the level of data availability and confidentiality, as it is unlikely that different CSPs are out of service at the same time or collude with each other to extract information of a user. This paper investigates the problem of storing data reliably and securely in multiple CSPs constrained by given budgets with minimum cost. Previous works, with variations in problem formulations, typically tackle the problem by decoupling it into sub-problems and solve them separately. While such a decoupling approach is simple, the resultant solution is suboptimal. This paper is the first one which considers the problem as a whole and derives a jointly optimal coding and storage allocation scheme, which achieves perfect secrecy with minimum cost. The analytical result reveals that the optimal coding scheme is the nested maximum-distance-separable code and the optimal amount of data to be stored in the CSPs exhibits a certain structure. The exact parameters of the code and the exact storage amount to each CSP can be determined numerically by simple 2-D search.

Review of energy storage allocation in power distribution networks: applications, methods and future research

Changes in the electricity business environment, dictated mostly by the increasing integration of renewable energy sources characterised by variable and uncertain generation, create new challenges especially in the liberalised market environment. The role of energy storage systems (ESS) is recognised as a mean to provide additional system security, reliability and flexibility to respond to changes that are still difficult to accurately forecast. However, there are still open questions about benefits these units bring to the generation side, system operators and the consumers. This study provides a comprehensive overview of the current research on ESS allocation (ESS sizing and siting), giving a unique insight into issues and challenges of integrating ESS into distribution networks and thus giving framework guidelines for future ESS research.

Storage allocation scheme for virtual instances of cloud computing

Cloud computing delivers resources and services through virtual machines on a pay-as-you-go basis. The allocation of storage space to users is usually determined by means of open allocation mechanisms that cannot guarantee an efficient allocation. Current allocation mechanisms do not consider user requests when making provisioning decisions. In other words, they assume that the storage spaces are fixed. In this study, we propose an algorithm for allocating storage spaces based on the requests of users. We present a unified storage allocation scheme (USAS) for cloud computing. USAS is a dynamic storage allocation framework for unlimited, limited, and free users. Our proposed approach is based on a storage partitioning policy, and we have compared our proposed scheme with open storage scheme and fixed storage scheme with common partition. We show through simulation study that USAS dynamically allocates space for different user requirements for all traffic loads.

An Improved Job Scheduling in Cloud Environment using Auto-Associative-Memory Network

Cloud computing plays a vital role in the field of information sharing through the internet. In this current scenario, some of the major problems in cloud computing are job scheduling, resource clustering, storage allocation, VM migration etc. In this paper, problems such as job scheduling and resource clustering are concentrated. Arrival time, waiting time, response time, makespan time, communication time, utilization time, throughput time of a job are considered while scheduling the jobs in a cloud environment. A good scheduling policy should achieve minimum makespan time. Efficiency of the storage and computation nodes is considered during the process of resource clustering. In this regard, a near optimal approach is proposed to perform job scheduling and resource clustering. Hopfield neural network (Auto-Associative-Memory network) is used along with equivalence partitioning recurrent node weight (EPRNW) algorithm to schedule the jobs in an excellent manner. The comparative study and performance evaluation is done between the existing approaches and proposed approach. The proposed approach minimizes the makespan time by using continuous Hopfield neural network to achieve the near optimal scheduling and equivalence partitioning recurrent node weight algorithm is used to identify the corresponding computation nodes instead of random selection of computation nodes for resource clustering. Efficiency of scheduling process is maximized and resource clustering is performed rapidly by using the proposed algorithm.

Fresh Food Distribution Center Storage Allocation Strategy Analysis Based on Optimized Entry-Item-Quantity-ABC

For labour-intensive field, appropriate storage location assignment is the best choice to increase order picking efficiency and reduce order cycle time, which satisfy customers and reduce cost at the same time. In this paper, we advance a storage location assignment for fresh food distribution center with a manual picker-to-parts picking system by using an optimized approach. To reflect the customer demand uncertainty, the orders received in a certain time range have been grouped and given the different coefficients according to the reference value of them. On that basis, the storage location can be designed optimally based on the Entry-Item-Quantity (EIQ) analysis, which can be used to resolve some orders picking issues, long-picking time and high inventory costs, caused by seasonal change of fresh food, unstable customer demand and repeat purchases. From the computational results, new storage allocation strategy achieves at most a 16% reduction in travel time.